Here’s a simple, clear Python example that shows how to train a small AI (machine learning) model using scikit-learn on sample data.

This example will:
✅ Create a synthetic dataset
✅ Build a logistic regression classifier
✅ Train the model
✅ Evaluate accuracy

I’ll add comments so it’s easy to follow:

python

<div class="code_bbcode"><div class="clearfix m-b-5"><strong>Code</strong><a class="pull-right m-t-0 btn btn-sm btn-default" href="../../includes/bbcodes/code_bbcode_save.php?thread_id=177&post_id=425&code_id=0"><i class="fa fa-download"></i> Download source</a></div><pre><code class="language-php"># Import required libraries

from sklearn.datasets import make_classification

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score



# Step 1: Create sample data

# Here, we generate 1000 samples, each with 5 features

X, y = make_classification(n_samples=1000, n_features=5, n_classes=2, random_state=42)



# Step 2: Split data into training and test sets (80% train, 20% test)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)



# Step 3: Create the model

model = LogisticRegression()



# Step 4: Train the model on training data

model.fit(X_train, y_train)



# Step 5: Make predictions on test data

y_pred = model.predict(X_test)



# Step 6: Evaluate the model

accuracy = accuracy_score(y_test, y_pred)

print(f"Model Accuracy: {accuracy:.2f}")</code></pre></div>

**What this example shows:**

* Creating synthetic data
* Splitting data into training & testing
* Training an AI model (logistic regression classifier)
* Evaluating accuracy